Background Synthesizing dance motions to match musical inputs is a significant challenge in animation research.Compared to functional human motions,such as locomotion,dance motions are creative and artistic,often infl...Background Synthesizing dance motions to match musical inputs is a significant challenge in animation research.Compared to functional human motions,such as locomotion,dance motions are creative and artistic,often influenced by music,and can be independent body language expressions.Dance choreography requires motion content to follow a general dance genre,whereas dance performances under musical influence are infused with diverse impromptu motion styles.Considering the high expressiveness and variations in space and time,providing accessible and effective user control for tuning dance motion styles remains an open problem.Methods In this study,we present a hierarchical framework that decouples the dance synthesis task into independent modules.We use a high-level choreography module built as a Transformer-based sequence model to predict the long-term structure of a dance genre and a low-level realization module that implements dance stylization and synchronization to match the musical input or user preferences.This novel framework allows the individual modules to be trained separately.Because of the decoupling,dance composition can fully utilize existing high-quality dance datasets that do not have musical accompaniments,and the dance implementation can conveniently incorporate user controls and edit motions through a decoder network.Each module is replaceable at runtime,which adds flexibility to the synthesis of dance sequences.Results Synthesized results demonstrate that our framework generates high-quality diverse dance motions that are well adapted to varying musical conditions and user controls.展开更多
A novel free form based face cartoon stylization method is presented in this paper. First, a face cartoon library with marked feature points is constructed. And then select the input image as the target image and an a...A novel free form based face cartoon stylization method is presented in this paper. First, a face cartoon library with marked feature points is constructed. And then select the input image as the target image and an appropriate cartoon image from cartoon library as the reference image, apply the deformation between the corresponding feature points of the images to the target image. Finally, we apply an image stylization process to the result image. As an alternative method, we also choose an appropriate cartoon image as the target image and the input image as the reference image to apply the free from deformation. The experimental results show that our method is straightforward and quick with diversified styles, delivering more infection.展开更多
Arbitrary style transfer aims to perceptually reflect the style of a reference image in artistic creations with visual aesthetics.Traditional style transfer models,particularly those using adaptive instance normalizat...Arbitrary style transfer aims to perceptually reflect the style of a reference image in artistic creations with visual aesthetics.Traditional style transfer models,particularly those using adaptive instance normalization(AdaIN)layer,rely on global statistics,which often fail to capture the spatially local color distribution,leading to outputs that lack variation despite geometric transformations.To address this,we introduce Patchified AdaIN,a color-inspired style transfer method that applies AdaIN to localized patches,utilizing local statistics to capture the spatial color distribution of the reference image.This approach enables enhanced color awareness in style transfer,adapting dynamically to geometric transformations by leveraging local image statistics.Since Patchified AdaIN builds on AdaIN,it integrates seamlessly into existing frameworks without the need for additional training,allowing users to control the output quality through adjustable blending parameters.Our comprehensive experiments demonstrate that Patchified AdaIN can reflect geometric transformations(e.g.,translation,rotation,flipping)of images for style transfer,thereby achieving superior results compared to state-of-the-art methods.Additional experiments show the compatibility of Patchified AdaIN for integration into existing networks to enable spatial color-aware arbitrary style transfer by replacing the conventional AdaIN layer with the Patchified AdaIN layer.展开更多
Background With the development of virtual reality(VR)technology,there is a growing need for customized 3D avatars.However,traditional methods for 3D avatar modeling are either time-consuming or fail to retain the sim...Background With the development of virtual reality(VR)technology,there is a growing need for customized 3D avatars.However,traditional methods for 3D avatar modeling are either time-consuming or fail to retain the similarity to the person being modeled.This study presents a novel framework for generating animatable 3D cartoon faces from a single portrait image.Methods First,we transferred an input real-world portrait to a stylized cartoon image using StyleGAN.We then proposed a two-stage reconstruction method to recover a 3D cartoon face with detailed texture.Our two-stage strategy initially performs coarse estimation based on template models and subsequently refines the model by nonrigid deformation under landmark supervision.Finally,we proposed a semantic-preserving face-rigging method based on manually created templates and deformation transfer.Conclusions Compared with prior arts,the qualitative and quantitative results show that our method achieves better accuracy,aesthetics,and similarity criteria.Furthermore,we demonstrated the capability of the proposed 3D model for real-time facial animation.展开更多
This paper examines presence of some stylized facts of short-term stock prices in the banking sector of the Nigerian Stock Market (NSM). Non-normality, lack of autocorrelation in the returns at first lag and significa...This paper examines presence of some stylized facts of short-term stock prices in the banking sector of the Nigerian Stock Market (NSM). Non-normality, lack of autocorrelation in the returns at first lag and significant positive autocorrelation in higher magnitude returns, widely studied in other markets, are investigated using daily closing stock prices of the four major Nigerian banks (Access, First, Guaranty Trust and United Bank for Africa (UBA)), from 2001 to 2013;encompassing periods of different financial scenarios. Jarque-Bera (JB), Doonik-Hansen, Kolmogrov-Smirnov and Ljung-Box (Q) test statistics are applied. Our findings reveal that the four banks stocks behave slightly different, but generally possess the stylized facts found in other markets. Observed is that, while the distributions of the returns for two of these banks (First and UBA) are approximately symmetric and leptokurtic;those of Access and Guaranty Trust banks are significantly non-symmetric and leptokurtic, thus non-normally distributed. Also established is that, while autocorrelation functions of daily returns are either negative or zero, those of both absolute returns and the squared returns are mostly positive. The autocorrelations of absolute returns are found to be predominantly positive and more persistent than those of the squared returns;indicating volatility clustering. Consequently, we conclude that the short-term stock prices of these banks behave like those of other markets. Some implications of the results for financial investment and stock market behaviour in the banking sector of NSM are discussed.展开更多
基金Supported by Startup Fund 20019495,McMaster University。
文摘Background Synthesizing dance motions to match musical inputs is a significant challenge in animation research.Compared to functional human motions,such as locomotion,dance motions are creative and artistic,often influenced by music,and can be independent body language expressions.Dance choreography requires motion content to follow a general dance genre,whereas dance performances under musical influence are infused with diverse impromptu motion styles.Considering the high expressiveness and variations in space and time,providing accessible and effective user control for tuning dance motion styles remains an open problem.Methods In this study,we present a hierarchical framework that decouples the dance synthesis task into independent modules.We use a high-level choreography module built as a Transformer-based sequence model to predict the long-term structure of a dance genre and a low-level realization module that implements dance stylization and synchronization to match the musical input or user preferences.This novel framework allows the individual modules to be trained separately.Because of the decoupling,dance composition can fully utilize existing high-quality dance datasets that do not have musical accompaniments,and the dance implementation can conveniently incorporate user controls and edit motions through a decoder network.Each module is replaceable at runtime,which adds flexibility to the synthesis of dance sequences.Results Synthesized results demonstrate that our framework generates high-quality diverse dance motions that are well adapted to varying musical conditions and user controls.
文摘A novel free form based face cartoon stylization method is presented in this paper. First, a face cartoon library with marked feature points is constructed. And then select the input image as the target image and an appropriate cartoon image from cartoon library as the reference image, apply the deformation between the corresponding feature points of the images to the target image. Finally, we apply an image stylization process to the result image. As an alternative method, we also choose an appropriate cartoon image as the target image and the input image as the reference image to apply the free from deformation. The experimental results show that our method is straightforward and quick with diversified styles, delivering more infection.
基金supported by the National Research Foundation of Korea (NRF)grant funded by the Korean government (MSIT) (No.2022R1A2C1004657,Contribution Rate:50%)Culture,Sports and Tourism R&D Program through the Korea Creative Content Agency grant funded by Ministry of Culture Sports and Tourism in 2024 (Project Name:Developing Professionals for R&D in Contents Production Based on Generative Ai and Cloud,Project Number:RS-2024-00352578,Contribution Rate:50%).
文摘Arbitrary style transfer aims to perceptually reflect the style of a reference image in artistic creations with visual aesthetics.Traditional style transfer models,particularly those using adaptive instance normalization(AdaIN)layer,rely on global statistics,which often fail to capture the spatially local color distribution,leading to outputs that lack variation despite geometric transformations.To address this,we introduce Patchified AdaIN,a color-inspired style transfer method that applies AdaIN to localized patches,utilizing local statistics to capture the spatial color distribution of the reference image.This approach enables enhanced color awareness in style transfer,adapting dynamically to geometric transformations by leveraging local image statistics.Since Patchified AdaIN builds on AdaIN,it integrates seamlessly into existing frameworks without the need for additional training,allowing users to control the output quality through adjustable blending parameters.Our comprehensive experiments demonstrate that Patchified AdaIN can reflect geometric transformations(e.g.,translation,rotation,flipping)of images for style transfer,thereby achieving superior results compared to state-of-the-art methods.Additional experiments show the compatibility of Patchified AdaIN for integration into existing networks to enable spatial color-aware arbitrary style transfer by replacing the conventional AdaIN layer with the Patchified AdaIN layer.
文摘Background With the development of virtual reality(VR)technology,there is a growing need for customized 3D avatars.However,traditional methods for 3D avatar modeling are either time-consuming or fail to retain the similarity to the person being modeled.This study presents a novel framework for generating animatable 3D cartoon faces from a single portrait image.Methods First,we transferred an input real-world portrait to a stylized cartoon image using StyleGAN.We then proposed a two-stage reconstruction method to recover a 3D cartoon face with detailed texture.Our two-stage strategy initially performs coarse estimation based on template models and subsequently refines the model by nonrigid deformation under landmark supervision.Finally,we proposed a semantic-preserving face-rigging method based on manually created templates and deformation transfer.Conclusions Compared with prior arts,the qualitative and quantitative results show that our method achieves better accuracy,aesthetics,and similarity criteria.Furthermore,we demonstrated the capability of the proposed 3D model for real-time facial animation.
文摘This paper examines presence of some stylized facts of short-term stock prices in the banking sector of the Nigerian Stock Market (NSM). Non-normality, lack of autocorrelation in the returns at first lag and significant positive autocorrelation in higher magnitude returns, widely studied in other markets, are investigated using daily closing stock prices of the four major Nigerian banks (Access, First, Guaranty Trust and United Bank for Africa (UBA)), from 2001 to 2013;encompassing periods of different financial scenarios. Jarque-Bera (JB), Doonik-Hansen, Kolmogrov-Smirnov and Ljung-Box (Q) test statistics are applied. Our findings reveal that the four banks stocks behave slightly different, but generally possess the stylized facts found in other markets. Observed is that, while the distributions of the returns for two of these banks (First and UBA) are approximately symmetric and leptokurtic;those of Access and Guaranty Trust banks are significantly non-symmetric and leptokurtic, thus non-normally distributed. Also established is that, while autocorrelation functions of daily returns are either negative or zero, those of both absolute returns and the squared returns are mostly positive. The autocorrelations of absolute returns are found to be predominantly positive and more persistent than those of the squared returns;indicating volatility clustering. Consequently, we conclude that the short-term stock prices of these banks behave like those of other markets. Some implications of the results for financial investment and stock market behaviour in the banking sector of NSM are discussed.
